No abstract
Abstract-Data dissemination is useful for many applications of Disruption Tolerant Networks (DTNs). Current data dissemination schemes are generally network-centric ignoring user interests. In this paper, we propose a novel approach for user-centric data dissemination in DTNs, which considers satisfying user interests and maximizes the cost-effectiveness of data dissemination. Our approach is based on a social centrality metric, which considers the social contact patterns and interests of mobile users simultaneously, and thus ensures effective relay selection. The performance of our approach is evaluated from both theoretical and experimental perspectives. By formal analysis, we show the lower bound on the costeffectiveness of data dissemination, and analytically investigate the tradeoff between the effectiveness of relay selection and the overhead of maintaining network information. By trace-driven simulations, we show that our approach achieves better costeffectiveness than existing data dissemination schemes.
Abstract-3G networks are currently facing severe traffic overload problems caused by excessive demands of mobile users. Offloading part of the 3G traffic through other forms of networks, such as Delay Tolerant Networks (DTNs), WiFi hotspots, and Femtocells, is a promising solution. However, since these networks can only provide intermittent and opportunistic connectivity to mobile users, utilizing them for 3G traffic offloading may result in a non-negligible delay. As the delay increases, the users' satisfaction decreases. In this paper, we investigate the tradeoff between the amount of traffic being offloaded and the users' satisfaction. We provide a novel incentive framework to motivate users to leverage their delay tolerance for 3G traffic offloading. To minimize the incentive cost given an offloading target, users with high delay tolerance and large offloading potential should be prioritized for traffic offloading. To effectively capture the dynamic characteristics of users' delay tolerance, our incentive framework is based on reverse auction to let users proactively express their delay tolerance by submitting bids. We further take DTN as a case study to illustrate how to predict the offloading potential of the users by using stochastic analysis. Extensive tracedriven simulations verify the efficiency of our incentive framework for 3G traffic offloading.
Abstract-Disruption Tolerant Networks (DTNs) are characterized by the low node density, unpredictable node mobility and lack of global network information. Most of current research efforts in DTNs focus on data forwarding, but only limited work has been done on providing effective data access to mobile users. In this paper, we propose a novel approach to support cooperative caching in DTNs, which enables the sharing and coordination of cached data among multiple nodes and reduces data access delay. Our basic idea is to intentionally cache data at a set of Network Central Locations (NCLs), which can be easily accessed by other nodes in the network. We propose an effective scheme which ensures appropriate NCL selection based on a probabilistic selection metric, and coordinate multiple caching nodes to optimize tradeoff between data accessibility and caching overhead. Extensive trace-driven simulations show that our scheme significantly improves data access performance compared to existing schemes.
Abstract-Node mobility and end-to-end disconnections in disruption-tolerant networks (DTNs) greatly impair the effectiveness of data forwarding. Although social-based approaches can address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we study multicast with single and multiple data items in DTNs from a social network perspective, develop analytical models for multicast relay selection, and furthermore investigate the essential difference between multicast and unicast in DTNs. The proposed approach selects relays according to their capabilities, measured by social-based metrics, for forwarding data to the destinations. The design of social-based metrics exploits social network concepts such as node centrality and social community, and the selected relays ensure achieving the required data delivery ratio within the given time constraint. Extensive trace-driven simulations show that the proposed approach has similar data delivery ratio and delay to that of Epidemic routing, but significantly reduces data forwarding cost, measured by the number of relays used.
Existing routing algorithms for Delay Tolerant Networks (DTNs) assume that nodes are willing to forward packets for others. In the real world, however, most people are socially selfish; i.e., they are willing to forward packets for nodes with whom they have social ties but not others, and such willingness varies with the strength of the social tie. Following the philosophy of design for user, we propose a Social Selfishness Aware Routing (SSAR) algorithm to cope with user selfishness and provide good routing performance in an efficient way. To select an effective forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, and derives a metric with mathematical modeling and machine learning techniques to measure the forwarding capability of the mobile nodes. Moreover, SSAR formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR) to satisfy user demands for selfishness and performance. Trace-driven simulations show that SSAR allows users to maintain selfishness and achieves good routing performance with low transmission cost.tunity with the destination. To further improve performance, SSAR formulates the forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR). It provides a heuristic-based solution that forwards the most effective packets for social selfishness and routing performance. Extensive trace-driven simulations show that SSAR can achieve good routing performance with low transmission cost.The remainder of this paper is structured as follows. Section 2 presents an overview of SSAR. Section 3 gives the detailed design. Section 4 introduces the trace-driven simulations and discusses the results. The last two sections present related work and conclusions, respectively. SSAR OverviewIn this section, we first introduce our design philosophy and then discuss our models and assumptions. Finally, wegive an overview of SSAR and explain how it works. Design for UserExisting work in mobile ad hoc networks and DTNs has focused on addressing individual selfishness using reputation-based [30], credit-based [14], or game-theory based [12] approaches to stimulate users to cooperate and forward packets for others. If the nodes cooperate with others, they will be able to get help from others; if not, they will be punished, e.g., being deprived of access to the network.These incentive-based schemes may not be directly applied to deal with social selfishness, since they do not consider social selfishness. In these schemes, every node has to provide service to others no matter there is a social tie or not. Thus, social selfishness is not allowed. In essence, these approaches follow the philosophy of "design for network" because they sacrifice the user's requirement for selfishness (i.e., resource saving) to achieve high performance.We address this problem from a different point of view. We allow social selfishness but also try to maintain good routing performance under social selfish behavior. Our underlying ph...
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